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Multi-tiered architecture for automatic sensor failure detection in traffic systems: edge-fog-cloud collaboration for enhanced accuracy
No full textNejad, MarkThe increasing reliance on traffic sensor networks in intelligent transportation systems requires robust methods for ensuring the accuracy and reliability of sensor data. Traditional fault detection techniques often struggle with the high volume of data generated by these sensors, the dynamic variability in traffic patterns, and the lack of sufficient failure data for training detection models. The problem becomes larger when we take into consideration the need to distribute the computational workload to minimize the stress and latency of the main traffic control server. ☐ A multi-tiered structure combined with machine learning models is an ideal framework for realizing these goals in near real-time. In the proposed system, the edge tier employs lightweight statistical models to flag anomalous sensor readings that deviate from expected thresholds. The fog tier clusters spatiotemporally-dependent sensors in the network. The cloud tier takes a sensor’s filtered data and compares it to other relevant sensor data within the same cluster and classifies whether the sensor is operating normally or anomalously. This multi-tiered approach allows for efficient processing by balancing localized data analysis at the edge with more sophisticated, large-scale processing in the cloud. ☐ Innovations are needed to overcome barriers in addressing these challenges of fault detection as well as the issue of fault detection in a spatiotemporal network system. This MS thesis introduces three main innovations: (1) a novel framework for fault detection in traffic sensor systems in non-dilemma zones; (2) a new use of a Density-Based Spatial Clustering of Applications with Noise model modified with dynamic time warping to detect sensor failures in traffic systems, which has not been explored in previous research; and (3) the systematic tuning of parameters of the machine learning models used for the specific context of traffic sensor failure detection.University of Delaware, Department of Computer and Information SciencesM.S
MITIGATING FOOD INSECURITY-RELATED STIGMA: A REVIEW OF INTERVENTION STRATEGIES
No full textenterObjective: To characterize intervention strategies addressing food insecurity-related
stigma implemented in federal nutrition programs (e.g. SNAP, WIC) and emergency
food programs (e.g. food pantries, food cupboards) within high income countries.
Data Source: Six databases (PubMed, PsychINFO, Web of Science, CINAHL,
Sociological Abstracts, Dissertations and Theses Global) and the Internet were
searched through September 2024.
Study Inclusion and Exclusion Criteria: Included articles were published in or
before September 2024, detailed intervention strategies in high income countries,
published in English, contained original research or evidence of an intervention on
food insecurity stigma.
Data Extraction: Data on study characteristics and stigma intervention characteristics
were extracted with a structured template.
Data Synthesis: Descriptive statistics and thematic analysis were used.
Results: The review found 46 intervention strategies across 18 articles. The majority
of articles were based in the United States (89.9%) with the remaining portion from
the United Kingdom (11.1%). Interventions most frequently targeted emergency food
(44.4% of articles, 70.3% of interventions). Interventions were most often operating at
the structural level (89.1%).
Conclusion: This review demonstrates the frequency of structural level interventions,
particularly within the emergency food setting, and the need to implement strategies
that address the everyday interactions between staff/volunteers and those seeking food
assistance.
Keywords: Stigma, Food Assistance, Food Insecurity, Interventions
Funding Acknowledgement: This paper was funded by Healthy Eating Research, a
national program of the Robert Wood Johnson Foundation.ente
Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy
No full textThis article was originally published in Journal of Vacuum Science & Technology A. The version of record is available at: https://doi.org/10.1116/6.0004493.
© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.The authors acknowledge the support and assistance of k-Space Associates, Inc. in the development of RHEED image capture and conversion approaches. This research was supported by the AT SCALE Initiative under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). The research used resources available through Research Computing at PNNL. PNNL is a multiprogram national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830. P.G. and R.C. gratefully acknowledge support for synthesis and characterization from the National Science Foundation (NSF) under Award No. DMR-2045993
Direct antagonism overrides induced systemic resistance response by Bacillus subtilis UD1022 against dollar spot pathogen in creeping bentgrass
No full textThis article was originally published in Plant Stress Journal. The version of record is available at: https://doi.org/10.1016/j.stress.2025.101158
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-
nd/4.0/ )Dollar spot, a major turfgrass disease, poses significant challenges to the turfgrass industry, highlighting the need for sustainable disease management strategies. Plant growth promoting rhizobacteria (PGPR) Bacillus subtilis strain UD1022 (hereafter UD1022) has previously shown ability to inhibit dollar spot pathogen, Clarireedia jacksonii through direct antagonism under in vitro conditions. However, additional investigation is needed to fully understand its role as a biocontrol agent in planta. It is unclear whether UD1022 suppresses dollar spot in creeping bentgrass, and if so, whether the suppression occurs primarily through induced plant defense responses or via direct antagonism. To address these knowledge gaps, we investigate the biocontrol potential of UD1022 against C. jacksonii in creeping bentgrass, focusing on systemic defense responses and direct antagonism mechanisms. Our data showed that plants primed with UD1022 and subsequently inoculated with foliar dollar spot pathogen did not protect plants against the fungal pathogen. In contrast, UD1022 direct application to the leaves with the pathogen led to a 43.6% reduction in disease severity. Gene expression data showed that UD1022 application to roots triggers foliar defense response dependent on jasmonic acid (JA) and salicylic acid (SA) pathways. In addition, plants treated with UD1022 mutants deficient in sporulation, surfactin, and biofilm formation pathways failed to protect plants against the dollar spot pathogen, suggesting the role of these pathways in disease protection and PGPR fitness. Understanding the mechanisms by which PGPR modulate plant immunity could contribute to the development of sustainable turfgrass management approaches.Authors acknowledge support from the United States Golf Association [grant number 2021–03–727]
DIET-DEPENDENT EFFECTS OF DIGESTION ON METABOLIC RATE OF CLEARNOSE SKATES (Rostroraja eglanteria)
No full textenterThis study aimed to describe the impact of diet on specific dynamic action
(SDA) in Clearnose Skates (Rostroraja eglanteria). Metabolic rates of mature female
Clearnose Skates (n=2) were calculated before and after being fed anchovies and
shrimp. Metabolic rates were measured using static respirometry. Skates had an
increase of up to 1.68x pre-feeding MO2 when eating anchovy, and an increase of up
to 1.31x pre-feeding MO2 when eating shrimp. Meal size was also found to increase
the duration and peak of the SDA effect. Minimizing the amount of energy occupied
by SDA makes feeding more efficient and provides more available energy for
Clearnose Skates.ente
THE IMPACT OF ESG ON HOTEL CORPORATE REPUTATION: THE MODERATING ROLE OF CASINO OPERATION
No full textDespite the ample empirical evidence on the ESG-firm outcome relationship, the implication of ESG on reputation, specifically in the hospitality industry, remains underexplored to date. Therefore, this study examined the ESG-corporate reputation relationship with moderating effect of casino operation. Drawing on Signaling and Legitimacy theories, this study addressed the current research by applying a linear regression with panel data drawn from the Thomson Reuters Refinitiv and Compustat database to analyze data on 24 public hotel firms in the United States between 2002 and 2023. Specifically, the result reveals that: environmental performance decreases corporate reputation, social performance has no significant effect on corporate reputation, while governance performance increases corporate reputation. In terms of the moderating effect, this study found that: casino operation positively moderates the environmental performance-corporate reputation relationship while negatively moderating both social performance-corporate reputation and governance performance-corporate reputation relationships. Theoretically, this study extends ESG -corporate reputation literature by providing a logical discussion about how each dimension of ESG is related to building a strong corporate reputation. In addition, this study advances the ESG studies in the field of hospitality management by introducing casino operations as a moderator factor, highlighting how industry-specific characteristics shape the ESG-corporate reputation relationship. Practically, this study provides insightful implications for industry practitioners in comprehending the individualized effect of ESG on corporate reputation
Seasonal Variation and Key Controls of Groundwater Ammonium Concentrations in Hypoxic/Anoxic Riparian Sediments
No full textThis article was originally published in Journal of Geophysical Research: Biogeosciences published by AGU. Published 2025 American Geophysical Union.
Sena, M. G., Peipoch, M., Joshi, B., Rahman, Md. M., Peck, E., Gold, A. J., et al. (2025). Seasonal variation and key controls of groundwater ammonium concentrations in hypoxic/anoxic riparian sediments. Journal of Geophysical Research: Biogeosciences, 130, e2023JG007900. https://doi.org/10.1029/2023JG007900. To view the published open abstract, go to https://doi.org/10.1029/2023JG007900.
© 2025. American Geophysical Union. All Rights Reserved.
This article will be embargoed until 07/30/2025.The seasonal controls of hydrology, temperature, hypoxia, and biogeochemical conditions for groundwater ammonium–N (NH4+) concentrations are not well understood. Here we investigated these controls for riparian groundwaters located upstream of two milldams over a period of 4 years. Groundwater chemistry was sampled monthly while groundwater elevations, hydraulic gradients, and temperatures were recorded sub-hourly. Distinct seasonal patterns for NH4+ were observed which differed among the wells. For wells that displayed a strong seasonal pattern, NH4+ concentrations increased through the summer and peaked in October–November. These elevated concentrations were attributed to ammonification, suppression of nitrification, and/or dissimilatory nitrate reduction to ammonium (DNRA). These processes were driven by high groundwater temperatures, low hydraulic gradients (or long residence times), hypoxic/anoxic groundwater conditions, and increased availability of dissolved organic carbon as an electron donor. In contrast, NH4+ concentrations decreased in the riparian groundwater from January to April during cool and wet conditions. A groundwater well with elevated total dissolved iron (TdFe) concentrations had elevated NH4+ concentrations but displayed a muted seasonal response. In addition to hydrologic controls, we attributed this response to additional NH4+ contribution from Fe-driven autotrophic DNRA and/or ammonification linked to dissimilatory Fe reduction. Understanding the temporal patterns and factors controlling NH4+ in riparian groundwaters is important for making appropriate watershed management decisions and implementing appropriate best management practices.
Plain Language Summary
Wetland or riparian soils and groundwaters depleted of oxygen can accumulate ammonium–N (NH4+), a toxic pollutant at high concentrations. NH4+ can be produced and removed via microbial processes that are influenced by seasonal factors such as: temperature, groundwater levels, dissolved oxygen, organic carbon, nitrate, and dissolved iron. In this study, we studied monthly grab samples of riparian groundwater collected upstream of milldams over a period of 4 years. We evaluated the data, identified three main seasons, and generated relationships between physical and chemical factors and NH4+ over time. The main findings from this study show that NH4+: (a) peaks in October and November when water is warm, stagnant, and high in organic carbon, (b) decreases from January – April when temperatures cooled and lowered microbial production, and (c) elevated iron groundwater concentrations can dampen these seasonal trends. Results from this study can help to improve the timing of milldam removal practices and for the management of wetlands that remove nitrogen from the terrestrial environment.
Key Points
- Groundwater ammonium–N concentrations peaked following warm water temperatures, low hydraulic gradients, and high dissolved organic carbon (DOC)
- Ammonium concentrations were likely driven by seasonal changes in ammonification, nitrification, and dissimilatory nitrate reduction to ammonium (DNRA)
- Hydrologic conditions and elevated dissolved iron (Fe) concentrations likely shaped the seasonal ammonium patternsWe thank the Koser and the Cooch families for permissions to work on their property. This study was funded by the National Science Foundation Hydrologic Sciences Grant 1929747 and 2213855. We also thank Dorothy Merritts, Robert Walter and Jessie Thomas-Blate for their support for the project. Editors and reviewers are also acknowledged for their constructive and careful reviews
Integration of Digital Image Correlation and finite element analysis for enhanced rail track modeling and modulus estimation
No full textHead, Monique H.Understanding vertical rail deflection is essential for evaluating track support conditions to minimize derailments and optimize maintenance strategies. The objective of this study is to integrate multi-point Digital Image Correlation (DIC) data from vertical rail deflections for developing more refined track models that reflect in-situ conditions. Utilizing a field-deployable DIC monitoring system, rail deflection profiles were captured from two field case studies with varying substructure characteristics to estimate rail deflections and overall track moduli. The field data were used to calibrate finite element (FE) models in ABAQUS. A parametric study was conducted to evaluate the effects of subgrade modulus, ballast modulus, ballast thickness, and subgrade depth on vertical track deflection. Results demonstrated a nonlinear but consistent decrease in rail deflection with increasing subgrade and ballast modulus. Ballast thickness and subgrade depth showed complex influences due to their role in modifying load path flexibility. A minimum ballast modulus of 25,000 psi was found necessary to maintain deflections within AREMA guidelines. The results also validated the Pasternak foundation model as a better representation of the observed track deflection behavior. This integrated approach enables more precise estimation of track stiffness, especially in transition zones, and supports the development of refined, data-driven models for rail infrastructure assessment. These findings can inform targeted maintenance, refined modeling approaches, and data-driven infrastructure assessments.University of Delaware, Department of Civil, Construction and Environmental EngineeringM.C.E
Decadal Variability in the Core Strength of the South Pacific Convergence Zone
No full textThis article was originally published in Geophysical Research Letters. The version of record is available at: https://doi.org/10.1029/2025GL116182
©2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License 9https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.The South Pacific Convergence Zone (SPCZ), the primary rainfall source for South Pacific
islands, exhibits decadal variability in its core region that remains insufficiently studied. Isolating sea surface
temperature internal variability (SSTIV) from external forcing (1940–2013) reveals SSTIV is the dominant driver
of SPCZ core strength variability (CSV), quantified via 10‐m wind convergence. Local SSTIV modulates CSV
through thermodynamic feedbacks, whereas equatorial Pacific sea surface temperature (SST) anomalies exert
limited remote influence. Regression analysis shows a 9.1 × 10 6 s 1 increase in CSV and a 1.7 mm day 1
increase in precipitation per 1°C SSTIV rise on decadal scales. Although external forcing significantly shapes
SPCZ SST, its direct impact on CSV is counteracted by the tropical Pacific SST gradient. The strong sensitivity
of CSV to internal SST variability, compared to its muted response to external forcing, underscores the pivotal
role of internal climate variability in shaping SPCZ dynamics.We thank the two anonymous reviewers
for their constructive comments. This
study is supported by the National Key
R&D Program of China (Grant
2023YFF0806500), the Natural Science
Foundation of Fujian Province of China
(2023J01021), the International
Partnership Program of Chinese Academy
of Sciences (059GJHZ2023104MI)
Exploring protein dynamics and stability with advanced neutron scattering techniques
No full textWagner, Norman J.Liu, YunUnderstanding the stability of protein-based therapeutics, particularly monoclonal antibodies (mAbs), is essential for ensuring their efficacy and longevity in biopharmaceutical applications. This dissertation investigates the intricate relationship between protein dynamics and thermal stability, driven by the need to develop advanced methods to assess long-term stability. Using bovine serum albumin (BSA) and the NIST monoclonal antibody (NISTmAb) as model systems, this research employs advanced neutron scattering techniques—Small-Angle Neutron Scattering (SANS) and Neutron Spin Echo (NSE) spectroscopy—to provide novel insights into protein dynamics and their relationship with the thermal stability. ☐ One important contribution of this work is the development and validation of a technique that uses Small-Angle Neutron Scattering (SANS) to measure hydrogen-deuterium exchange (HDX) in proteins. HDX assesses protein dynamics by quantifying the exchange of solvent-accessible hydrogen atoms with deuterium, which reflects the protein's conformational stability. The application of SANS in this context, termed HDX-SANS, offers a non-invasive approach to observe the HDX of proteins in their folded state, formulated in their buffer solutions. HDX-SANS complements other HDX methods, like HDX mass spectrometry, which is destructive and can be sensitive to formulation conditions. BSA was used first to demonstrate the noninvasive and quantitative capabilities of HDX-SANS, including the measurement of temperature dependent exchange rates and the determination of an activation energy of the HDX for BSA, which is found to be 81 ± 1 kJ/mol. ☐ Building on these findings, HDX-SANS was applied to NISTmAb under various formulation conditions, using an anionic Hofmeister series of sodium salts as excipients, including sulfate (SO42⁻), perchlorate (ClO4⁻), and thiocyanate (SCN⁻). NISTmAb is a standard mAb widely used by industry, which is publicly accessible. Its structural similarity to many mAbs on the market ensures that these findings are broadly applicable to a wide range of therapeutics. Our experimental results show that different types of salts have a strong impact on the HDX. The ranked order of HDX dynamics is observed to be: Na2SO4 < NaClO4 < NaSCN, which is consistent with both the anticipated ranked order of stability associated with the Hofmeister series and the effects of these anions on protein thermal stability, measured by differential scanning calorimetry. This alignment between the ranked HDX dynamics of different NISTmAb formulations and their corresponding melting temperatures suggests that the HDX dynamics observed in this study are consistent with the thermal stability of NISTmAb across various formulation conditions. ☐ While HDX in proteins provides an indirect measurement of intraprotein domain dynamics, to further understand mAb stability in formulation, the internal domain dynamics of NISTmAb are directly measured using NSE spectroscopy. NSE is a powerful technique, uniquely capable of probing nanometer and nanosecond-scale dynamics—precisely the relevant length and time scales for capturing the individual domain motions of an antibody. The analysis of the NSE results indicate that internal domain motions increase as the NISTmAb formulations approach their thermal transition temperature. This finding suggests that internal domain dynamics likely play an important role in the thermal stability of mAbs. In summary, the observations discovered in this dissertation advance our understanding of how protein dynamics are linked to thermal stability. The novel techniques and detailed findings presented offer a robust foundation for future research that could help the development of more stable and effective protein-based therapeutics.University of Delaware, Department of Biomedical EngineeringPh.D